Automated Entity Identification for Efficient Profiling in an Event Probability Prediction System

ABSTRACT

A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/399,067 filed on Feb. 17, 2012, entitled “Automated EntityIdentification For Efficient Profiling In An Event ProbabilityPrediction System” and is a continuation and claims the benefit ofpriority under 35 U.S.C. §120 of U.S. patent application Ser. No.12/110,261, filed Apr. 25, 2008, entitled “Automated EntityIdentification For Efficient Profiling In An Event ProbabilityPrediction System”, which the disclosures of the priority applicationsare incorporated by reference herein.

BACKGROUND

This disclosure relates generally to a computer-based, real-time systemfor event probability prediction that implements an efficient profilingtechnology which minimizes required computer resources and allows forthe identification of entities exhibiting anomalous behavior.

Computer-based event probability prediction systems traditionally usesome amount of historical information, a profile, about individualobjects in order to compare present behavior with past behavior. Each ofthese objects is defined to be an entity, while a set of similar objectsis defined to be an entity class. Examples of events to predict includewhether or not a loan applicant will default on a loan and whether ornot a credit card transaction is fraudulent. Examples of entitiesinclude a particular customer account at a bank and a particularAutomatic Teller Machine (ATM).

To achieve high performance, an event probability prediction systemoften includes a mathematical model or combination of models whichextracts patterns from historical data and uses the patterns on thepresent transaction data to calculate a score, a number that representsthe likelihood that a particular event will occur. The model or modelsin the system traditionally need to store and access the profile forevery existing entity in the entity class (e.g. every ATM beingconsidered in the problem). Limitations of computer resources requirethat such a large amount of information is maintained in a disk-residentprofile database, external to the computer program forming the core ofthe event probability prediction system. This leads to several issues inthe development and running of the event probability prediction system:

1) It is necessary to create an interface between the mathematical modeland the external database containing the profiles during development ofthe event probability prediction system.

2) It is necessary to create an interface between the mathematical modeland the external database containing the profiles in the productionenvironment in which the system will ultimately be used.

3) The system's capacity to process transactions may be severely limiteddue to the required interface with an external database.

Each of these issues could be a potential problem making the developmentand/or installation of the event probability prediction systeminfeasible.

Furthermore, in addition to the strain a traditional system places onthe computer resources available, such a system may not allow the userto easily identify those entities which display a behavior of interest,particularly when multiple entity classes are being profiled to providea multi-dimensional view of the data. Effective event probabilityprediction requires that only the minimum set of entities, a set whosemembership varies over time, be profiled and maintained in a data store.It would be advantageous to provide a system and method that solves anyof or any combination of the problems disclosed hereinabove.

SUMMARY

This document presents a new computer-based event probability predictionsystem and method that has two main advantages over previous systems.First, the new system uses computer resources more efficiently whichallows it to achieve faster execution times and simplifiedimplementation. Second, the new system allows for the identification andreporting of those entities which display anomalous behavior when viewedacross multiple dimensions of the data and within a higher risk set ofentities. The core of the system and method is a specialized profilingthat efficiently maintains historical information only on a small numberof entities rather than on all of the entities in a particular entityclass. The resulting type of profile, a Concise Profile, uses AutomatedEntity Identification (AEI) which allows a large disk-resident profiledatabase to be replaced with a small dynamic table stored in memory. AConcise Profile consists of 1) an online-updated, importance-ranked AEItable that contains the profile records for a concise subset of entitiesand 2) a recycling algorithm, based on an objective function related tothe probability of a particular event, that determines the dynamicmembership of the table. The system and method further calculatesstatistics on the AEI table to identify outliers, entities which exhibitanomalous behavior, to be reported to users of the system independent ofthe main score(s).

In one aspect, a computer-implemented method includes steps of defininga first subset of entities belonging to one or more entity classes, andconstructing at least one historical profile for each entity in thesubset of entities based on a set of possible outcomes of transactionbehavior of each entity in the first subset of entities. Based on thehistorical profiles, a second subset of entities having transactionbehavior associated with a transaction is selected, the transactionbehavior being predictive of at least one targeted outcome from the setof possible outcomes. The method further includes the step of redefiningthe first subset of entities with the second subset of entities.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects will now be described in detail with referenceto the following drawings.

FIG. 1 shows an overview of the flow of data records through the eventprobability prediction system.

FIG. 2 shows the steps involved in updating the AEI table for theConcise Profile.

FIG. 3 shows a possible configuration of the system in which a feedbackloop is added.

FIG. 4 shows a possible configuration of the system in which the ConciseProfile variables are used to augment the output score of a base model.

FIG. 5 shows a possible configuration of the system in which the ConciseProfile variables are not blended with the base model output, but areused as input to the base model.

FIG. 6 shows a possible configuration of the system in which the ConciseProfile variables are used to augment a base model and the output of thebase model is used as additional input for the updating of the AEItable.

FIG. 7 shows a possible configuration of the system identical to thatshown in FIG. 4 except for the addition of feedback loops.

FIG. 8 shows a possible configuration of the system in which the ConciseProfile variables are used to augment a base model only when the basemodel score lies in a desired range.

FIG. 9 shows a possible configuration of the system identical to thatshown in FIG. 8 except for the addition of feedback loops.

FIG. 10 shows a table illustrating average transactions per hour ofspecific entities.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

This document describes a computer-based, real-time system for eventprobability prediction implementing efficient profiling technology whichminimizes required computer resources and allows for the identificationof entities exhibiting anomalous behavior.

The invention applies to event probability prediction in general but toaid in the description the details of the invention are discussed belowusing a fraud detection system as a specific instance of the invention.In the context of a fraud detection system, the system calculates, forexample, the probability that a financial transaction is fraudulent.

In contrast to existing systems for fraud detection, the systemdescribed here uses a specialized profiling technology, called a ConciseProfile, to efficiently maintain historical information only on a smallnumber of entities at any given time rather than on all of the entitiesin an entity class. At the heart of the concept of a Concise Profile isAutomated Entity Identification (AEI), which provides a way to replace alarge, disk-resident profile database with a small dynamic table, offixed maximum size, stored in memory rather than in an externaldatabase.

Whereas existing fraud detection systems maintain historical informationfor every entity in an entity class, a Concise Profile provides anonline-updated, importance-ranked AEI table that contains profilerecords for a concise subset of entities whose dynamic membership isdetermined by a recycling algorithm, described in more detail below.This recycling algorithm is based on an objective function which usescriteria that are related to the probability that the transaction isassociated with fraud.

Conceptually, each row in the AEI table corresponds to a single entityand contains two types of information: 1) information used to determinethe rank of the entity in the table and 2) Concise Profile variables.The recycling algorithm ensures that entities exhibiting the behavior ofinterest (e.g. apparent fraud) have a consistently high rank in the AEItable, while other entities have a lower rank or may be removed from thetable.

In addition to the benefits of reduced computer resource usage due tothe maintenance of profiles for only a subset of all entities, the AEItable allows the invention to achieve something traditional systems areunable to do easily. The entities which are represented in the AEI tableat any given time form a carefully selected, high-risk subset of theentire entity class by virtue of the recycling algorithm. One cancompute statistics on this concise, ranked list of entity profiles todetermine which entities, if any, are outliers in their behaviorcompared to all other entities represented on the list. In this sensethe system provides the identities of those entities which exhibitanomalous (i.e. risky) behavior compared to other, already high-riskentities. As they do not require a large share of the computerresources, many Concise Profiles can be used simultaneously in a singlefraud detection system. These profiles can be constructed to monitormany different entity classes and even different aspects of a singleentity class by using a variety of recycling algorithms. The system canuse information from the various AEI tables to detect anomalies that mayonly be visible when viewed from multiple dimensions (i.e. usingmultiple Concise Profiles). Furthermore, once an anomaly is detected,the identity of the corresponding entity is known and can be used in aseparate, dedicated system to handle such anomalies or can be includedin a report sent to a human fraud analyst. This ability to detect,identify, and report in real time or near real time on the entitieswhich exhibit anomalous behavior when viewed from several dimensionsgives the invention a significant advantage over traditional frauddetection systems.

An overview of the flow of data records through an example frauddetection system is shown in FIG. 1. Each new data record, receivedsequentially, corresponds to a single transaction involving the entityclasses being used in the Concise Profiles. At least one Concise Profiletable is required, but multiple Concise Profile tables, which each havea limited view into fraud, are required if multiple entity classes arebeing profiled as indicated in FIG. 1 and in other figures. First, a newdata record 102 (e.g. an ATM transaction) to be evaluated for itslikelihood of fraud enters the system. At step 104 the data in therecord is used to update the AEI table for the entity class implementedas Concise Profile 1. In the case of multiple Concise Profiles, each onecorresponds to a different entity class and each has a corresponding AEItable. All AEI tables are updated using information in the data record.The values of the variables stored in the AEI tables for the entitiesinvolved in the current transaction are output to the Concise Profilemodel at step 106 which uses the variables to evaluate (score) therecord.

At step 108 the score and the input data record are passed to a casemanagement system in which human fraud experts determine whether therecord is actually a fraudulent record or a non-fraudulent record. Thecase management system allows the fraud expert to access other datasources including communicating with the legitimate person who isauthorized to conduct the transaction (e.g. the credit card holder).

FIG. 2 illustrates the steps involved in updating the AEI table for theConcise Profile. After a new record 202 is presented to the system, atstep 204 the system reduces the importance rating of each row in the AEItable. These importance ratings are used to rank order the entitiesrepresented in the table. The importance rating update procedure followsa mathematical formula which can be symbolized by the equation.

f _(new) =g(f _(old) , X)

where f_(new) represents the new importance rating of a particular rowin the table, f_(old) represents the importance rating that takes intoaccount all records up to but not including the new record, X representsthe collection of information from the new record that may be useful inupdating the importance rating, and g( )represents the function whoseoutput is the new importance rating.

As a simple example of an importance rating, the recycling algorithm mayensure that entities occurring in frequent or recent transactions have aconsistently high rank in the AEI table, while other entities have alower rank. In this case entities which occur rarely in transactionswould have rare, short-lived appearances in the table. The importancerating then becomes equivalent to a frequency. To achieve this behavior,the updated frequency for each entity in the table can be recomputed onevery input transaction according to the equation.

f _(j) =α×f _(j-1)+β_(j)

where and f_(j) are f_(j-1) the values of the decayed frequency for aparticular entity in the table after the j′th and (j-1)'th transactions,a is a value between zero and one and is used to reduce all of theimportance ratings in the table (step 204), and β_(j) is zero if thej′th transaction did not involve the particular entity for which thedecayed frequency is being computed or one if the j′th transaction didinvolve the particular entity for which the decayed frequency is beingcomputed.

Updating the frequency in such a way that the most recent or frequententities maintain high ranks may be beneficial, for example, when thesystem is attempting to detect frauds which manifest themselves asbursts of activity at particular Automatic Teller Machines (ATMs). Inthis scenario, the frequency can be related to different objectivefunctions such as the number of transactions involving a card used forthe first-time at the ATM, the number of transactions in which theamount is greater than some threshold, the number of transactions with abase model score greater than some threshold, etc.

On the other hand, one can imagine updating the frequencies such thatthe least frequently transacting entities maintain their high ranks Thiswould be useful for any fraud detection system in which the occurrencesof the entities involved mostly follow some regular patterns whichinvolve regular time intervals. A bank customer may regularly writechecks every month to pay bills. These transactions would correspond toentities with low ranks in the AEI table, thus allowing the model toconcentrate on the rarer transactions which are more likely to befraudulent. Any other importance rating may be used in place of thefrequency as needed to ensure that entities displaying the behavior ofinterest are kept in the AEI table.

At step 206 the system calculates a key value which uniquely identifiesthe entity within the entity class. At step 208 the system uses the keyvalue to determine whether or not the entity is already represented inthe AEI table.

If the entity is not already represented in the AEI table, then itbecomes a candidate to be added with its importance rating set to apredetermined initial value. This addition occurs in one of thefollowing two ways depending on whether or not the table is already full(i.e. all available rows are occupied), a determination made in step210. If the table is full then in step 212 the system determines whetherthe initial importance rating is greater than the importance rating ofthe lowest-ranked entity in the table. If this condition is notsatisfied, then at step 214 the system outputs the default values of theConcise Profile variables and the new entity will not have its profilerecord added to the table. It is possible to configure the system insuch a way that the condition is always satisfied, so entities not foundin the table always have their profile record added to the table. If thecondition is satisfied then at step 216 the system removes the profilerecord for the lowest ranked entity in the table, which is the entityhaving the lowest frequency among all entities represented in the table.This step, together with step 204, ensures that profile records forentities which have low importance ratings will be replaced in the tableby profile records for entities from more recent transactions. At step218 the profile record for the new entity is added to the table with theinitial importance rating value. If at step 210 it is determined thatthe table is not already full, then the system proceeds directly to step218 in which the profile record for the new entity is added to the tablewith the initial importance rating value. At step 220 the ConciseProfile variables defined for this entity class are calculated for thisentity using information in the record 202.

If the profile record for the entity already exists in the AEI table,then from step 208 the system proceeds directly to step 222 in which theimportance rating of the entity is increased. This step ensures that anentity with important behavior maintains a high rank in the table. Atstep 224 the Concise Profile variables defined for this entity class areupdated for this entity using information in the record 202.

After the Concise Profile variables for the entity are either calculatedfor the first time (step 220) or updated (step 224), at step 226 thesystem re-ranks the entities represented in the table based onimportance rating. At step 228 the system outputs the values of theConcise Profile variables for the entity.

One example of an entity that could be used in a fraud detection systemis an ATM. For example, a criminal with a large number ofcounterfeit/stolen debit cards, each corresponding to a separatecustomer account, may successively use the cards at a single ATM toremove money from each account. This type of fraudulent behavior wouldcause the affected ATM to be highly ranked in the AEI table (assuming animportance rating update equation that emphasizes recent or frequenttransactions) which would allow the fraud detection system at step 104to maintain constantly updated Concise Profile variables describing thebehavior of this particular ATM. Wisely constructed variables chosen toreflect differences between patterns of fraudulent behavior and patternsof non-fraudulent behavior would be evaluated by the Concise Profilemodel (step 106) and produce a score consistent with the transactions atthis ATM being fraudulent. The advantages of the proposed frauddetection system are evident in this application when one considers thata traditional system might need to maintain profiles on hundreds ofthousands of ATMs whereas the AEI table in the proposed system onlyneeds to store information on a few hundred ATMs at any one time toachieve significant fraud detection capability. Furthermore, potentialusers of a system (e.g. banks) may not be able to afford maintaining afull profile database containing all ATMs in addition to the traditionalfull profile database containing all bank customers. To reduce costsusers may be forced to choose a system which contains no ATM profiling,thereby losing sight of any ATM dynamics and restricting themselves to aone-dimensional, card-holder view of the data.

If the fraud patterns are static there is no need to create a systemwhich monitors and reacts to changing patterns of fraud. In the realworld in which the system must operate, however, fraud may be dynamic,manifesting itself in different ways at different times. FIG. 3 shows apossible configuration of the system identical to that shown in FIG. 1except for the addition of a feedback loop in which information flowsfrom case management at step 308 directly to the update of the AEI tableat step 304. In the context of a fraud detection system being describedhere, the purpose of the feedback is to allow the system to rapidlyincorporate new information on changing fraud patterns. The feedbackconsists of a confirmed fraud/nonfraud tag, the data for the recordcorresponding to the tag, and the model score for the record. Thefeedback improves the performance of the system in environmentscontaining a significant amount of dynamic fraud. One of the uses ofthis technique is to update risk tables of different entities to reflectthe most current fraud risk in the production environment.

An existing fraud detection system (one not using Concise Profiles) maycontain one or more mathematical models which calculate a scorerepresenting the likelihood that the new data record corresponds to afraudulent transaction. If these models, represented by the processlabeled Base Model at step 404 in FIG. 4, already achieve a relativelyhigh level of fraud detection then it may make sense to keep them in thesystem, using the Concise Profile variables to augment the base modeloutput. This is depicted in FIG. 4. The new data record 402 is used asinput both to the base model at step 404 and to the AEI table update atstep 406. The output from step 404 includes the scores from each ofthose models as well as the values of the variables created by thosemodels. The Concise Profile variables from step 406 are blended(mathematically combined) at step 408 with the output from the basemodel. In this way the system produces a final score which is sent tothe case management step 410. The final blended score can moreaccurately estimate the likelihood of fraud than the base model scorealone. For example, the base model may maintain profiles aboutindividual bank customers while a Concise Profile may maintainhistorical information about individual ATMs. This difference enablesthe Concise Profile variables to capture a complementary dimension ofthe data which is likely to increase the performance of the final scoreover that of the base model.

The base model may be sufficiently complex and flexible that it canhandle the Concise Profile variables being used at its input. In thissituation, depicted in FIG. 5, the input to the base model consists ofthe new data record as well as all Concise Profile variables. This hasthe potential to create a high performance system (i.e. one attaining ahigh level of fraud detection) because all of the available informationis accessible to the mathematical model at the same time. One drawback,however, is that the internal operation of the base model necessarilychanges if the Concise Profile variables contain information which helpto discriminate between fraud and nonfraud records. If this change inthe base model is not desired or not feasible, then the configuration ofFIG. 4 described above may be preferred. In the system of FIG. 4 thebase model remains unchanged by the addition of the Concise Profilevariables into the system.

On the other hand, one can consider an alternative modification of thesystem in FIG. 4. The base model remains unchanged but its output isused to create one or more of the Concise Profile variables. This isdepicted in FIG. 6 in which the output of step 604 is used at the inputto step 606. The Concise Profiles are able to incorporate into theirvariables the score or scores from the base model as well as thevariables created by the base model.

When the Concise Profiles are used to augment a base model it may stillbe beneficial to incorporate feedback into the updating of the AEItables, for the same reasons as those mentioned above in the discussionin reference to FIG. 3. Whether the Concise Profiles form the core ofthe system or augment a base model, the purpose of the feedback is toallow the system to rapidly incorporate new information on changingfraud patterns. FIG. 7 shows a configuration in which feedback is usedto update the Concise Profile variables which are then blended with thebase model output.

It may be desirable to configure the fraud detection system such thatthe Concise Profile variables are used to augment a base model onlyunder certain conditions. The final score sent to the case managementstep could be the base model score itself, except when this score is ina desired range. The desired range can be set, for example, to be thescore range in which business operating points are set and where theaccuracy of the predictions are the most crucial. This configuration ofthe system is illustrated in FIG. 8. It is similar to the systemdescribed in reference to FIG. 4 except for the addition of the decisionstep 808. At step 808 the base model score is examined. If the score isin the desired range then the base model output is sent to step 810 andblended with the Concise Profile variables. In this situation the scoresent to the case management step 812 is a blending of the base modeloutput and the Concise Profile variables. If, however, the base modelscore is not in the desired range then the score sent to the casemanagement step 812 is the unmodified base model score. In thisconfiguration of the system, the Concise Profile variables are able toimprove the performance of the fraud detection system by augmenting thebase model only when augmentation can be most beneficial.

FIG. 9 shows another system that is similar to the system described inreference to FIG. 4 and FIG. 8, except for the addition of the decisionstep 908 and a feedback loop. At step 908 the base model score isexamined. If the score is in the desired range then the base modeloutput is sent to step 910 and blended with the Concise Profilevariables. In this situation the score sent to the case management step912 is a blending of the base model output and the Concise Profilevariables. If, however, the base model score is not in the desired rangethen the score sent to the case management step 912 is the unmodifiedbase model score. When a feedback loop is added to the system, theresult is the system illustrated in FIG. 9. Many other systemconfigurations of various combinations of the main features describedabove (e.g. multiple Concise Profiles, feedback loops, blending based ona score range, concise Profile variables used as input to base model,base model output used as input to AEI table updates, etc.) can beimplemented.

In any of the configurations described above, specific statisticscalculated on the subset of entities represented in the AEI tables canbe used to identify entities exhibiting anomalous behavior. We refer tothis analysis on the AEI table as Automatic Identity Identification(AII). Consider the example of using the ATM entity class in a frauddetection system. The AEI table will contain a small subset of ATMsdeemed riskier than other ATMs in the data. If the average number oftransactions per hour with amount greater than $300 at a particular ATMis indicative of fraud, then this quantity can be calculated across allentities in the AEI table. The table of FIG. 10 shows a simple examplein which most of the ATMs have a value in the range two to four of thisquantity. One of the ATMs, however, has a value 15.4, much larger thanthe average value. If outliers such as this have a value which isgreater than, for example, ten standard deviations away from the meanvalue of the statistic being calculated, then the system has identified(hence the term Automatic Identity Identification) an extreme outlier(i.e. an ATM exhibiting the riskiest behavior) and can report it to ahuman fraud analyst or police authorities for further investigation orimmediate action. This type of identification and reporting of outlierswould be difficult to achieve for a traditional fraud detection systemthat uses a full profile database given the computational expense ofrunning regular queries on the full set of ATMs and having only limitedviews of the fraud characteristics. Doing Automatic IdentityIdentification analysis on several small concise profile tables enablesfast and efficient detection of outliers at a variety of dimensionsassociated with the fraud behaviors.

Some or all of the functional operations described in this specificationcan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof them. Embodiments of the invention can be implemented as one or morecomputer program products, i.e., one or more modules of computer programinstructions encoded on a computer readable medium, e.g., a machinereadable storage device, a machine readable storage medium, a memorydevice, or a machine-readable propagated signal, for execution by, or tocontrol the operation of, data processing apparatus.

The term “data processing apparatus” encompasses all apparatus, devices,and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of them. Apropagated signal is an artificially generated signal, e.g., amachine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also referred to as a program, software, anapplication, a software application, a script, or code) can be writtenin any form of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program can be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to, a communication interface toreceive data from or transfer data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto optical disks, oroptical disks.

Moreover, a computer can be embedded in another device, e.g., a mobiletelephone, a personal digital assistant (PDA), a mobile audio player, aGlobal Positioning System (GPS) receiver, to name just a few.Information carriers suitable for embodying computer programinstructions and data include all forms of non volatile memory,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto optical disks; and CD ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the invention canbe implemented on a computer having a display device, e.g., a CRT(cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofsuch back end, middleware, or front end components. The components ofthe system can be interconnected by any form or medium of digital datacommunication, e.g., a communication network. Examples of communicationnetworks include a local area network (“LAN”) and a wide area network(“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Certain features which, for clarity, are described in this specificationin the context of separate embodiments, may also be provided incombination in a single embodiment. Conversely, various features which,for brevity, are described in the context of a single embodiment, mayalso be provided in multiple embodiments separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Particular embodiments of the invention have been described. Otherembodiments are within the scope of the following claims. For example,the steps recited in the claims can be performed in a different orderand still achieve desirable results. In addition, embodiments of theinvention are not limited to database architectures that are relational;for example, the invention can be implemented to provide indexing andarchiving methods and systems for databases built on models other thanthe relational model, e.g., navigational databases or object orienteddatabases, and for databases having records with complex attributestructures, e.g., object oriented programming objects or markup languagedocuments. The processes described may be implemented by applicationsspecifically performing archiving and retrieval functions or embeddedwithin other applications.

What is claimed:
 1. A computer-implemented method comprising:generating, by the one or more data processors, a historical profile foreach entity in a first subset of entities, the historical profile beingbased on a set of possible outcomes of transaction behavior of eachentity, the first subset of entities belonging to one or more entityclasses; selecting, by the one or more data processors, a second subsetof entities based on the historical profiles, each entity in the secondsubset of entities having transaction behavior predictive of at leastone targeted outcome from the set of possible outcomes of transactionbehavior; generating, by the one or more data processors, a historicalprofile for each entity in the second subset of entities; combining, bythe one or more data processors, the second subset of entities with thefirst subset of entities to generate a third subset of entities; andmonitoring, by the one or more data processors, the historical profilesfor the third set of entities for transaction behavior indicative of afraud process.
 2. The computer-implemented method of claim 1, whereinthe targeted outcome is selected from the set of possible outcomes thatconsists of a desirable outcome and an undesirable outcome.
 3. Thecomputer-implemented method of claim 1, wherein the targeted outcomeincludes the fraud process in connection with the transaction behavior.4. The computer-implemented method of claim 1, wherein at least oneentity class includes automatic teller machines.
 5. Thecomputer-implemented method of claim 1, further comprising: generating,by the one or more data processors, at least one score related to thetransaction based on one or more mathematical models; and combining theat least one score with information of the transaction behavior togenerate a probability of the targeted outcome.
 6. Thecomputer-implemented method of claim 1, further comprising: generating,by the one or more data processors, at least one score related to thetransaction based on one or more mathematical models; and combining theat least one score with information from the historical profiles for theentities involved in the transaction to produce an accurate score. 7.The computer-implemented method of claim 6, wherein the historicalprofiles are used only if the accurate score is within a predeterminedrange of values that indicate a high probability of the targetedoutcome.
 8. The computer-implemented method of claim 1, furthercomprising generating an estimate of the probability of the targetedoutcome based only on the historical profiles.
 9. Thecomputer-implemented method of claim 1, wherein the values in thehistorical profile for a selected entity are computed using informationfrom transaction records that involved the selected entity.
 10. Thecomputer-implemented method of claim 9, wherein the values in thehistorical profile for an entity are computed using input variables andoutput scores computed for the transaction records by one or moremathematical models.
 11. A system comprising: at least one programmableprocessor; and a machine-readable medium storing instructions that, whenexecuted by the at least one processor, cause the at least oneprogrammable processor to perform operations comprising: generate ahistorical profile for each entity in a first subset of entities, thehistorical profile being based on a set of possible outcomes oftransaction behavior of each entity, the first subset of entitiesbelonging to one or more entity classes; select a second subset ofentities based on the historical profiles, each entity in the secondsubset of entities having transaction behavior predictive of at leastone targeted outcome from the set of possible outcomes of transactionbehavior; generate a historical profile for each entity in the secondsubset of entities; combine the second subset of entities with the firstsubset of entities to generate a third subset of entities; and monitorby the one or more data processors, the historical profiles for thethird set of entities for transaction behavior indicative of a fraudprocess.
 12. The system of claim 11, wherein the targeted outcome isselected from the set of possible outcomes that consists of a desirableoutcome and an undesirable outcome.
 13. The system of claim 11, whereinthe targeted outcome includes the fraud process in connection with thetransaction behavior.
 14. The system of claim 11, wherein at least oneentity class includes automatic teller machines.
 15. The system of claim11, further comprising generating an estimate of the probability of thetargeted outcome based only on the historical profiles.
 16. The systemof claim 11, in which the values in the historical profile for aselected entity are computed using information from transaction recordsthat involved the selected entity.
 17. The system of claim 16, in whichthe values in the historical profile for an entity are computed usinginput variables and output scores computed for the transaction recordsby one or more mathematical models.